ame.boot: Bootrapping zur Bestimmung der Standardfehler und...

In umasds/mlame: Estimate Average Marginal Effects for Machine Learning Algorithms

Description

Bootrapping zur Bestimmung der Standardfehler und 95%-Konfidenzintervalle

Usage

ame.boot(data.name, rep = 100, meth = "dt", func, var.name, fromtoby)

Examples

## Not run: 
ame.boot(test.data, 10, "lm", y ~ alter + geschl + bildung, "geschl")
ame.boot(test.data, 10, "dtt", y ~ alter+ bildung, "geschl")
ame.boot(test.data, 10, "dt", y ~ alter + geschl + bildung, "bildung")
ame.boot(test.data, 10, "dt", y ~ alter + geschl, "alter", seq(20,60,1))

## End(Not run)

umasds/mlame documentation built on May 3, 2019, 2:24 p.m.